Enhanced granular medium-based tube press hardening

Active and passive control strategies of internal pressure for hot forming of tubes and profiles with granular media are described. Force transmission and plastic deformation of granular medium is experimentally investigated. Friction between tube, granular medium and die as also the external stress field are shown to be essential for the process understanding. Wrinkling, thinning and insufficient forming of the tube establishes the process window for the active pressure process. By improving the punch geometry and controlling tribological conditions, the process limits are extended. Examples for the passive pressure process reveal new opportunities for hot forming of tubes and profiles.Comment: 4 pages, 11 figure

A3\text{A}^3: Activation Anomaly Analysis

Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in strict semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpassing current baseline methods. Our semi-supervised anomaly detection method allows to inspect large amounts of data for anomalies across various applications.Comment: The first two authors contributed equally to this wor

Physical Adversarial Examples for Multi-Camera Systems

Neural networks build the foundation of several intelligent systems, which, however, are known to be easily fooled by adversarial examples. Recent advances made these attacks possible even in air-gapped scenarios, where the autonomous system observes its surroundings by, e.g., a camera. We extend these ideas in our research and evaluate the robustness of multi-camera setups against such physical adversarial examples. This scenario becomes ever more important with the rise in popularity of autonomous vehicles, which fuse the information of several cameras for their driving decision. While we find that multi-camera setups provide some robustness towards past attack methods, we see that this advantage reduces when optimizing on multiple perspectives at once. We propose a novel attack method that we call Transcender-MC, where we incorporate online 3D renderings and perspective projections in the training process. Moreover, we motivate that certain data augmentation techniques can facilitate the generation of successful adversarial examples even further. Transcender-MC is 11% more effective in successfully attacking multi-camera setups than state-of-the-art methods. Our findings offer valuable insights regarding the resilience of object detection in a setup with multiple cameras and motivate the need of developing adequate defense mechanisms against them

Vibro-fluidized beds: A systematic dynamics study utilizing Diffusing Wave Spectroscopy

Using a granular vibration fluidised bed, we demonstrate how our granular model system of polystyrene spheres becomes denser over time through different excitation amplitudes and how the heterogeneous dynamics of the system can be resolved with diffusing wave spectroscopy(DWS) measurements. We extract mean-square displacements from the DWS correlation functions of the sinusoidal excited system and model the excitation to extract the ballistic and diffusive time constants, as well as caging sizes, depending on applied acceleration and excitation time. At low excitations we observe a sub-diffusion power law behaviour of the MSD indicating potentially a glassy system